Name
Abstract | ||
|---|---|---|
In this paper, we introduce a new suction detection approach based on online learning of a Gaussian Mixture Model (GMM) with constrained parameters to model the reduction in pump flow signals baseline during suction events. A novel three-step methodology is employed: i) signal windowing, ii) GMM based classification and iii) GMM parameter adaptation. More specifically, the first 5 second segment is used for the parameter initialization and the consequent 1 second windows are classified and used for model adaptation. The proposed approach has been tested in simulation (pump flow) signals and satisfactory results have been obtained. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/BIBE.2012.6399661 | BIBE |
Keywords | DocType | Citations |
pump flow,Gaussian Mixture Model,suction event,online learning,model adaptation,GMM parameter adaptation,proposed approach,new suction detection approach,rotary blood pump,parameter initialization,novel three-step methodology | Conference | 1 |
PageRank | References | Authors |
0.48 | 5 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Libera Fresiello | 1 | 3 | 2.16 |
| Markos G. Tsipouras | 2 | 372 | 30.51 |
| Dimitrios I. Fotiadis | 3 | 941 | 121.32 |
| Alexandros T. Tzallas | 4 | 225 | 27.88 |
| Evaggelos C. Karvounis | 5 | 17 | 3.94 |
| George Rigas | 6 | 72 | 11.38 |
| Krzysztof Zieliński | 7 | 224 | 39.01 |
| Y. Goletsis | 8 | 126 | 16.41 |
| Maria Giovanna Trivella | 9 | 46 | 8.30 |